[Virtual reality and digital biomarkers: a clinical tool for early autism diagnosis]. / Realidad virtual y biomarcadores digitales: una herramienta clínica para el diagnóstico del autismo.

INTRODUCTION: Autism Spectrum Disorder (ASD) is a neurodevelopmental condition which traditional assessment procedures encounter certain limitations. The current ASD research field is exploring and endorsing innovative methods to assess the disorder early on, based on the automatic detection of biomarkers. However, many of these procedures lack ecological validity in their measurements. In this context, virtual reality (VR) shows promise for objectively recording biosignals while users experience ecological situations. METHODS: This study outlines a novel and playful VR procedure for the early assessment of ASD, relying on multimodal biosignal recording. During a VR experience featuring 12 virtual scenes, eye gaze, motor skills, electrodermal activity and behavioural performance were measured in 39 children with ASD and 42 control peers. Machine learning models were developed to identify digital biomarkers and classify autism. RESULTS: Biosignals reported varied performance in detecting ASD, while the combined model resulting from the combination of specific-biosignal models demonstrated the ability to identify ASD with an accuracy of 83% (SD = 3%) and an AUC of 0.91 (SD = 0.04). DISCUSSION: This screening tool may support ASD diagnosis by reinforcing the outcomes of traditional assessment procedures.

Introducción: El Trastorno del Espectro Autista (TEA) es un trastorno del neurodesarrollo, y sus procedimientos tradicionales de evaluación encuentran ciertas limitaciones. El actual campo de investigación sobre TEA está explorando y respaldando métodos innovadores para evaluar el trastorno tempranamente, basándose en la detección automática de biomarcadores. Sin embargo, muchos de estos procedimientos carecen de validez ecológica en sus mediciones. En este contexto, la realidad virtual (RV) presenta un prometedor potencial para registrar objetivamente bioseñales mientras los usuarios experimentan situaciones ecológicas. Métodos: Este estudio describe un novedoso y lúdico procedimiento de RV para la evaluación temprana del TEA, basado en la grabación multimodal de bioseñales. Durante una experiencia de RV con 12 escenas virtuales, se midieron la mirada, las habilidades motoras, la actividad electrodermal y el rendimiento conductual en 39 niños con TEA y 42 compañeros de control. Se desarrollaron modelos de aprendizaje automático para identificar biomarcadores digitales y clasificar el autismo. Resultados: Las bioseñales reportaron un rendimiento variado en la detección del TEA, mientras que el modelo resultante de la combinación de los modelos de las bioseñales demostró la capacidad de identificar el TEA con una precisión del 83% (DE = 3%) y un AUC de 0.91 (DE = 0.04). Discusión: Esta herramienta de detección puede respaldar el diagnóstico del TEA al reforzar los resultados de los procedimientos tradicionales de evaluación.

The whole-body motor skills of children with autism spectrum disorder taking goal-directed actions in virtual reality.

Many symptoms of the autism spectrum disorder (ASD) are evident in early infancy, but ASD is usually diagnosed much later by procedures lacking objective measurements. It is necessary to anticipate the identification of ASD by improving the objectivity of the procedure and the use of ecological settings. In this context, atypical motor skills are reaching consensus as a promising ASD biomarker, regardless of the level of symptom severity. This study aimed to assess differences in the whole-body motor skills between 20 children with ASD and 20 children with typical development during the execution of three tasks resembling regular activities presented in virtual reality. The virtual tasks asked to perform precise and goal-directed actions with different limbs vary in their degree of freedom of movement. Parametric and non-parametric statistical methods were applied to analyze differences in children's motor skills. The findings endorsed the hypothesis that when particular goal-directed movements are required, the type of action could modulate the presence of motor abnormalities in ASD. In particular, the ASD motor abnormalities emerged in the task requiring to take with the upper limbs goal-directed actions with low degree of freedom. The motor abnormalities covered (1) the body part mainly involved in the action, and (2) further body parts not directly involved in the movement. Findings were discussed against the background of atypical prospective control of movements and visuomotor discoordination in ASD. These findings contribute to advance the understanding of motor skills in ASD while deepening ecological and objective assessment procedures based on VR.

Intervenciones tecnológicas adaptativas para el trastorno del espectro autista: una revisión bibliográfica / Adaptive technological interventions for autism spectrum disorder: a literature review

Resumen Los individuos con trastornos del espectro autista suelen describirse con deficiencias comunicativas, sociales, emocionales y de comportamiento. A menudo están aislados y son pasivos, con pocas oportunidades de interacción positiva y constructiva con el mundo exterior. Por otra parte, pueden mostrar comportamientos retraídos, estereotipados y disruptivos. Estas condiciones pueden dificultar seriamente sus habilidades adaptativas al ambiente, con consecuencias negativas en su calidad de vida. La heterogeneidad fenotípica y la manifestación, así como la gravedad de los síntomas, pueden variar considerablemente según el niño. Esos determinan la necesidad de intervenciones personalizadas y adaptivas según las necesidades específicas, incluyendo factores como la edad, las habilidades intelectuales y las áreas afectadas específicas. Una intervención temprana promovería las habilidades adaptativas, la autodeterminación y la autonomía hacia el entorno. No obstante, los tiempos de esperas y los costes no permiten una evaluación temprana y como consecuencia los tiempos de intervención se retrasan afectando la cualidad de vida de los niños y de los pa dres. Además, los programas tradicionales de intervención dependen de la experiencia de los terapeutas. Una posible forma de superar este problema es el uso de tecnología adaptativa objetiva según las necesidades. El objetivo de este artículo es proporcionar una visión general de las pruebas empíricas disponible en los últimos siete años. En total, se seleccionaron 8 estudios, con 132 participantes que utilizaron 4 sistemas tecnológicos. Por último, se discuten las implicaciones tecnológicas, clínicas, psicológicas y rehabilitadoras de los hallazgos. Se esbozaron directrices prácticas dentro de esta área temática como perspectivas de investigación futuras.

Abstract Individuals with autistic spectrum disorder are often described as having communication, social, emo tional, and behavioral impairments. They are often isolated and passive, with few opportunities for positive and constructive interaction with the outside world. Moreover, they may exhibit withdrawn, stereotyped and disruptive behaviors. The aforementioned conditions can seriously hamper their ability to adapt to the environment, with negative consequences on their quality of life. Phenotypic heterogeneity and manifestation, as well as symptom severity, can vary greatly from child to child. These determine the need for individualized and adaptive interventions according to specific needs, including factors such as age, intellectual ability, and specific affected areas. Early intervention would promote adaptive skills, self-determination, and autonomy towards the environment. However, wait times and costs do not allow for early assessment, and therefore intervention times are delayed, affecting the quality of life of children and parents. In addition, traditional intervention programs depend on the expertise of the therapists. One possible way to overcome this problem is by using objective adaptive technologies based on needs. This article aims to provide an overview of the empirical evidence available over the past seven years. Overall, 8 studies were selected, with 132 participants using 4 technological systems. Finally, the technological, clinical, psychological, and rehabilitative implications of the findings are discussed. Practical guidelines within this topic area are outlined as future research perspectives.

[Adaptive technological interventions for autism spectrum disorder: a literature review]. / Intervenciones tecnológicas adaptativas para el trastorno del espectro autista: una revisión bibliográfica.

Individuals with autistic spectrum disorder are often described as having communication, social, emotional, nd behavioral impairments. They are often isolated and passive, with few opportunities for positive and constructive interaction with the outside world. Moreover, they may exhibit withdrawn, stereotyped and disruptive behaviors. The aforementioned conditions can seriously hamper their ability to adapt to the environment, with negative consequences on their quality of life. Phenotypic heterogeneity and manifestation, as well as symptom severity, can vary greatly from child to child. These determine the need for individualized and adaptive interventions according to specific needs, including factors such as age, intellectual ability, and specific affected areas. Early intervention would promote adaptive skills, self-determination, and autonomy towards the environment. However, wait times and costs do not allow for early assessment, and therefore intervention times are delayed, affecting the quality of life of children and parents. In addition, traditional intervention programs depend on the expertise of the therapists. One possible way to overcome this problem is by using objective adaptive technologies based on needs. This article aims to provide an overview of the empirical evidence available over the past seven years. Overall, 8 studies were selected, with 132 participants using 4 technological systems. Finally, the technological, clinical, psychological, and rehabilitative implications of the findings are discussed. Practical guidelines within this topic area are outlined as future research perspectives.

Los individuos con trastornos del espectro autista suelen describirse con deficiencias comunicativas, sociales, emocionales y de comportamiento. A menudo están aislados y son pasivos, con pocas oportunidades de interacción positiva y constructiva con el mundo exterior. Por otra parte, pueden mostrar comportamientos retraídos, estereotipados y disruptivos. Estas condiciones pueden dificultar seriamente sus habilidades adaptativas al ambiente, con consecuencias negativas en su calidad de vida. La heterogeneidad fenotípica y la manifestación, así como la gravedad de los síntomas, pueden variar considerablemente según el niño. Esos determinan la necesidad de intervenciones personalizadas y adaptivas según las necesidades específicas, incluyendo factores como la edad, las habilidades intelectuales y las áreas afectadas específicas. Una intervención temprana promovería las habilidades adaptativas, la autodeterminación y la autonomía hacia el entorno. No obstante, los tiempos de esperas y los costes no permiten una evaluación temprana y como consecuencia los tiempos de intervención se retrasan afectando la cualidad de vida de los niños y de los padres. Además, los programas tradicionales de intervención dependen de la experiencia de los terapeutas. Una posible forma de superar este problema es el uso de tecnología adaptativa objetiva según las necesidades. El objetivo de este artículo es proporcionar una visión general de las pruebas empíricas disponible en los últimos siete años. En total, se seleccionaron 8 estudios, con 132 participantes que utilizaron 4 sistemas tecnológicos. Por último, se discuten las implicaciones tecnológicas, clínicas, psicológicas y rehabilitadoras de los hallazgos. Se esbozaron directrices prácticas dentro de esta área temática como perspectivas de investigación futuras.

Eye gaze as a biomarker in the recognition of autism spectrum disorder using virtual reality and machine learning: A proof of concept for diagnosis.

The core symptoms of autism spectrum disorder (ASD) mainly relate to social communication and interactions. ASD assessment involves expert observations in neutral settings, which introduces limitations and biases related to lack of objectivity and does not capture performance in real-world settings. To overcome these limitations, advances in technologies (e.g., virtual reality) and sensors (e.g., eye-tracking tools) have been used to create realistic simulated environments and track eye movements, enriching assessments with more objective data than can be obtained via traditional measures. This study aimed to distinguish between autistic and typically developing children using visual attention behaviors through an eye-tracking paradigm in a virtual environment as a measure of attunement to and extraction of socially relevant information. The 55 children participated. Autistic children presented a higher number of frames, both overall and per scenario, and showed higher visual preferences for adults over children, as well as specific preferences for adults' rather than children's faces on which looked more at bodies. A set of multivariate supervised machine learning models were developed using recursive feature selection to recognize ASD based on extracted eye gaze features. The models achieved up to 86% accuracy (sensitivity = 91%) in recognizing autistic children. Our results should be taken as preliminary due to the relatively small sample size and the lack of an external replication dataset. However, to our knowledge, this constitutes a first proof of concept in the combined use of virtual reality, eye-tracking tools, and machine learning for ASD recognition. LAY SUMMARY: Core symptoms in children with ASD involve social communication and interaction. ASD assessment includes expert observations in neutral settings, which show limitations and biases related to lack of objectivity and do not capture performance in real settings. To overcome these limitations, this work aimed to distinguish between autistic and typically developing children in visual attention behaviors through an eye-tracking paradigm in a virtual environment as a measure of attunement to, and extraction of, socially relevant information.

Application of Supervised Machine Learning for Behavioral Biomarkers of Autism Spectrum Disorder Based on Electrodermal Activity and Virtual Reality.

OBJECTIVE: Sensory processing is the ability to capture, elaborate, and integrate information through the five senses and is impaired in over 90% of children with autism spectrum disorder (ASD). The ASD population shows hyper-hypo sensitiveness to sensory stimuli that can generate alteration in information processing, affecting cognitive and social responses to daily life situations. Structured and semi-structured interviews are generally used for ASD assessment, and the evaluation relies on the examiner's subjectivity and expertise, which can lead to misleading outcomes. Recently, there has been a growing need for more objective, reliable, and valid diagnostic measures, such as biomarkers, to distinguish typical from atypical functioning and to reliably track the progression of the illness, helping to diagnose ASD. Implicit measures and ecological valid settings have been showing high accuracy on predicting outcomes and correctly classifying populations in categories. METHODS: Two experiments investigated whether sensory processing can discriminate between ASD and typical development (TD) populations using electrodermal activity (EDA) in two multimodal virtual environments (VE): forest VE and city VE. In the first experiment, 24 children with ASD diagnosis and 30 TDs participated in both virtual experiences, and changes in EDA have been recorded before and during the presentation of visual, auditive, and olfactive stimuli. In the second experiment, 40 children have been added to test the model of experiment 1. RESULTS: The first exploratory results on EDA comparison models showed that the integration of visual, auditive, and olfactive stimuli in the forest environment provided higher accuracy (90.3%) on sensory dysfunction discrimination than specific stimuli. In the second experiment, 92 subjects experienced the forest VE, and results on 72 subjects showed that stimuli integration achieved an accuracy of 83.33%. The final confirmatory test set (n = 20) achieved 85% accuracy, simulating a real application of the models. Further relevant result concerns the visual stimuli condition in the first experiment, which achieved 84.6% of accuracy in recognizing ASD sensory dysfunction. CONCLUSION: According to our studies' results, implicit measures, such as EDA, and ecological valid settings can represent valid quantitative methods, along with traditional assessment measures, to classify ASD population, enhancing knowledge on the development of relevant specific treatments.

[Autism spectrum disorder biomarkers based on biosignals, virtual reality and artificial intelligence]. / Biomarcadores del trastorno del especto autista basados en bioseñales, realidad virtual e inteligencia artificial.

It has been observed that the stratification of Autism Spectrum Disorders (ASD) generated by the current scales is not effective for the personalization of early treatments. The clinical evaluation of ASD requires its consideration as a continuum of deficits, and there is a need to identify biologically significant parameters (biomarkers) that have the power to automatically characterize each individual at different stages of neurological development. The emerging field of computational psychiatry (CP) attempts to meet the needs of precision diagnosis by developing powerful computational and mathematical techniques. A growing scientific activity proposes the use of implicit measures based on biosignals for the classification of ASD. Virtual reality (VR) technologies have demonstrated potential for ASD interventions, but most of the work has used virtual reality for the learning / objective of interventions. Very few studies have used biological signals for recording and detailed analysis of behavioral responses that can be used to monitor or produce changes over time. In this paper the concept of behavioral biomarkers based on VR or VRBB is introduced. VRBB will allow the classification of ASD using a paradigm of computational psychiatry based on implicit brain processes measured through psychophysiological signals and the behavior of subjects exposed to complex replicas of social conditions using virtual reality interfaces.

Se ha observado que la estratificación de trastornos del espectro autista (TEA) generada por las escalas actuales no es efectiva para la personalización de tratamientos tempranos. La evaluación clínica de TEA requiere su consideración como un continuo de déficits, y existe la necesidad de identificar parámetros biológicamente significativos (biomarcadores) que tengan el poder de caracterizar automáticamente a cada individuo en diferentes etapas del desarrollo neurológico. El incipiente campo de la psiquiatría computacional (CP) intenta satisfacer las necesidades de diagnóstico de precisión mediante el desarrollo de potentes técnicas computacionales y matemáticas. Una creciente actividad científica propone el uso de medidas implícitas basadas en bioseñales para la clasificación de ASD. Las tecnologías de realidad virtual (VR) han demostrado potencial para las intervenciones de TEA, pero la mayoría de los trabajos han utilizado la realidad virtual para el aprendizaje / objetivo de las intervenciones. Muy pocos estudios han utilizado señales biológicas para el registro y el análisis detallado de las respuestas conductuales que se pueden utilizar para monitorear o producir cambios a lo largo del tiempo. En el presente trabajo se introduce el concepto de biomarcadores conductuales basados en VR o VRBB. Los VRBB van a permitir la clasificación de TEA utilizando un paradigma de psiquiatría computacional basado en procesos cerebrales implícitos medidos a través de señales psicofisiológicas y el comportamiento de sujetos expuestos a complejas réplicas de condiciones sociales utilizando interfaces de realidad virtual.

Biomarcadores del trastorno del especto autista basados en bioseñales, realidad virtual e inteligencia artificial / Autism spectrum disorder biomarkers based on biosignals, virtual reality and artificial intelligence

Se ha observado que la estratificación de trastornos del espectro autista (TEA) generada por las escalas actuales no es efectiva para la personalización de tratamientos tempranos. La evaluación clínica de TEA requiere su consideración como un continuo de déficits, y existe la necesidad de identificar parámetros biológicamente significativos (biomarcadores) que tengan el poder de caracterizar automáticamente a cada individuo en diferentes etapas del desarrollo neurológico. El incipiente campo de la psiquiatría computacional (CP) intenta satisfacer las necesidades de diagnóstico de precisión mediante el desarrollo de potentes técnicas computacionales y matemáticas. Una creciente actividad científica propone el uso de medidas implícitas basadas en bioseñales para la clasificación de ASD. Las tecnologías de realidad virtual (VR) han demostrado potencial para las intervenciones de TEA, pero la mayoría de los trabajos han utilizado la realidad virtual para el aprendizaje / objetivo de las intervenciones. Muy pocos estudios han utilizado señales biológicas para el registro y el análisis detallado de las respuestas conductuales que se pueden utilizar para monitorear o producir cambios a lo largo del tiempo. En el presente trabajo se introduce el concepto de biomarcadores conductuales basados en VR o VRBB. Los VRBB van a permitir la clasificación de TEA utilizando un paradigma de psiquiatría computacional basado en procesos cerebrales implícitos medidos a través de señales psicofisiológicas y el comportamiento de sujetos expuestos a complejas réplicas de condiciones sociales utilizando interfaces de realidad virtual.

It has been observed that the stratification of Autism Spectrum Disorders (ASD) generated by the current scales is not effective for the personalization of early treatments. The clinical evaluation of ASD requires its consideration as a continuum of deficits, and there is a need to identify biologically significant parameters (biomarkers) that have the power to automatically characterize each individual at different stages of neurological development. The emerging field of computational psychiatry (CP) attempts to meet the needs of precision diagnosis by developing powerful computational and mathematical techniques. A growing scientific activity proposes the use of implicit measures based on biosignals for the classification of ASD. Virtual reality (VR) technologies have demonstrated potential for ASD interventions, but most of the work has used virtual reality for the learning / objective of interventions. Very few studies have used biological signals for recording and detailed analysis of behavioral responses that can be used to monitor or produce changes over time. In this paper the concept of behavioral biomarkers based on VR or VRBB is introduced. VRBB will allow the classification of ASD using a paradigm of computational psychiatry based on implicit brain processes measured through psychophysiological signals and the behavior of subjects exposed to complex replicas of social conditions using virtual reality interfaces.